To solve this problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of acres for planting daisies,
- $x_2$ as the number of acres for planting peonies.

The objective function is to maximize profit. The profit per acre from daisies is $55, and from peonies is $80. Therefore, the total profit can be represented as:

\[ \text{Maximize: } 55x_1 + 80x_2 \]

The constraints are:
1. The total budget for seeds cannot exceed $3000. Given that daisies cost $20 per acre and peonies cost $35 per acre, we have:
\[ 20x_1 + 35x_2 \leq 3000 \]
2. The total land used cannot exceed 100 acres:
\[ x_1 + x_2 \leq 100 \]
3. Bob cannot plant a negative number of acres, so both $x_1$ and $x_2$ must be non-negative:
\[ x_1 \geq 0, x_2 \geq 0 \]

In symbolic representation with natural language objects substituted for symbolic variables, the problem can be described as:

```json
{
'sym_variables': [('x1', 'acres of daisies'), ('x2', 'acres of peonies')],
'objective_function': '55*x1 + 80*x2',
'constraints': ['20*x1 + 35*x2 <= 3000', 'x1 + x2 <= 100', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code in Python to solve this optimization problem:

```python
from gurobipy import *

# Create a new model
m = Model("Agricultural_Land_Optimization")

# Create variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="daisies_acres")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="peonies_acres")

# Set the objective function
m.setObjective(55*x1 + 80*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(20*x1 + 35*x2 <= 3000, "budget_constraint")
m.addConstr(x1 + x2 <= 100, "land_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Daisies acres: {x1.x}")
    print(f"Peonies acres: {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```